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Using LLMs to enhance our testing practices

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Re: Using LLMs to enhance our testing practices

#11

Post by wenc »

satisfice wrote: post #4

Like nearly all the articles about AI doing "testing" or any other skilled activity, the last part of it admits that it is an unreliable method. What I don't see in this article-- which I suspect is because they haven't done any-- is any description of a competent and reasonably complete testing process of this method of writing "tests." What they probably did is to try this, feel good about it (because testing is no…

I do use LLMs to bootsrap my unit testing (because there is a lot boilerplate in unit tests and mocks), but I tend to finish the unit tests myself. This gives me confidence that my tests are accurate to the best of my knowledge.

Having good tests allows me to be more liberal with LLMs on implementation. I still only use LLMs to bootstrap the implementation, and I finish it myself. LLMs, being generative, are really good for ideating different implementations (it proposes implementations that I would never have thought of), but I never take any implementation as-is -- I always try to step through it and finish it off manually.

Some might argue that it'd be faster if I wrote the entire thing myself, but it depends on the problem domain. So much of what I do is involve implementing code for unsolved problems (I'm not writing CRUD apps for instance) that I really do get a speed-up from LLMs.

I imagine folks writing conventional code might spend more time fixing LLM mistakes and thus think that LLMs slow them down. But this is not true for my problem domain.

Re: Using LLMs to enhance our testing practices

#12

Post by sdesol »

hitradostava wrote: post #9
simonw wrote: post #7

Earlier quoted context omitted.

The answer to this is code review. If an LLM writes code for you - be it implementation or tests - you review it before you land it. If you don't understand how the code works, don't approve it. Sure, complacent developers will get burned. They'll find plenty of other non-AI ways to burn themselves too.

100% agree. We don't expect human developers to be perfect, why should we expect AI assistants. Code going to production should go through review. I do think that LLMs will increase the volume of bad code though. I use Cursor a lot, and occasionally it will produce perfect code, but often I need to direct and refine, and sometimes throw away. But I'm sure many devs will get lazy and just push once they've got the thi…

> 100% agree. We don't expect human developers to be perfect, why should we expect AI assistants.

I think the issue is that we are currently being sold that it is. I'm blown away by how useful AI is, and how stupid it can be at the same time. Take a look at the following example:

https://app.gitsense.com/?doc=f7419bfb27c896&highlight=&othe...

If you click on the sentence, you can see how dumb Sonnet-3.5 and GPT-4 can be. Each model was asked to spell-check and grammar-check the sentence 5 times each, and you can see that GPT-4o-mini was the only one that got this right all 5 times. The other models mostly got it comically wrong.

I believe LLM is going to change things for the better for developers, but we need to properly set expectations. I suspect this will be difficult, since a lot of VC money is being pumped into AI.

I also think a lot of mistakes can be prevented if you include in your prompt, how and why it did what it did. For example, the prompt that was used in the blog post should include "After writing the test, summarize how each rule was applied."

Re: Using LLMs to enhance our testing practices

#13

Post by simonw »

sdesol wrote: post #12
hitradostava wrote: post #9

Earlier quoted context omitted.

100% agree. We don't expect human developers to be perfect, why should we expect AI assistants. Code going to production should go through review. I do think that LLMs will increase the volume of bad code though. I use Cursor a lot, and occasionally it will produce perfect code, but often I need to direct and refine, and sometimes throw away. But I'm sure many devs will get lazy and just push once they've got the thi…

> 100% agree. We don't expect human developers to be perfect, why should we expect AI assistants. I think the issue is that we are currently being sold that it is. I'm blown away by how useful AI is, and how stupid it can be at the same time. Take a look at the following example: https://app.gitsense.com/?doc=f7419bfb27c896&highlight=&othe... If you click on the sentence, you can see how dumb Sonnet-3.5 and GPT-4 can…

"I think the issue is that we are currently being sold that it is."

The message that these systems are flawed appears to be pretty universal to me:

ChatGPT footer: "ChatGPT can make mistakes. Check important info."

Claude footer: "Claude can make mistakes. Please double-check responses."

https://www.meta.ai/ "Messages are generated by AI and may be inaccurate or inappropriate."

etc etc etc.

I still think the problem here is science fiction. We have decades of sci-fi telling us that AI systems never make mistakes, but instead will cause harm by following their rules too closely (paperclip factories, 2001: A Space Odyssey etc).

Turns out the actual AI systems we have make mistakes all the time.

Re: Using LLMs to enhance our testing practices

#14

Post by renegade-otter »

In every single system I have worked on, tests were not just tests - they were their own parallel application, and it required careful architecture and constant refactoring in order for it to not get out of hand.

"More tests" is not the goal - you need to write high impact tests, you need to think about how to test the most of your app surface with least amount of test code. Sometimes I spend more time on the test code than the actual code (probably normal).

Also, I feel like people would be inclined to go with whatever the LLM gives them, as opposed to really sitting down and thinking about all the unhappy paths and edge cases of UX. Using an autocomplete to "bang it out" seems foolish.

Re: Using LLMs to enhance our testing practices

#16

Post by sdesol »

simonw wrote: post #13
sdesol wrote: post #12

Earlier quoted context omitted.

> 100% agree. We don't expect human developers to be perfect, why should we expect AI assistants. I think the issue is that we are currently being sold that it is. I'm blown away by how useful AI is, and how stupid it can be at the same time. Take a look at the following example: https://app.gitsense.com/?doc=f7419bfb27c896&highlight=&othe... If you click on the sentence, you can see how dumb Sonnet-3.5 and GPT-4 can…

"I think the issue is that we are currently being sold that it is." The message that these systems are flawed appears to be pretty universal to me: ChatGPT footer: "ChatGPT can make mistakes. Check important info." Claude footer: "Claude can make mistakes. Please double-check responses." https://www.meta.ai/ "Messages are generated by AI and may be inaccurate or inappropriate." etc etc etc. I still think the problem…

You do have to admit, the footer is extremely small and it's also not in the most prominent place. I think most "AI companies" probably don't go into a sales pitch saying "It's awesome, but it might be full of shit".

I do see your science fiction angle, but I think the bigger issue is the media, VCs, etc. are not clearly spelling out that we are nowhere near science fiction AI.

Re: Using LLMs to enhance our testing practices

#17

Post by satisfice »

johnjwang wrote: post #5
satisfice wrote: post #4

Like nearly all the articles about AI doing "testing" or any other skilled activity, the last part of it admits that it is an unreliable method. What I don't see in this article-- which I suspect is because they haven't done any-- is any description of a competent and reasonably complete testing process of this method of writing "tests." What they probably did is to try this, feel good about it (because testing is no…

Author here: Yes, there are certain functions where writing good tests will be difficult for an LLM, but in my experience I've found that the majority of functions that I write don't need anything out of the ordinary and are relatively straightforward. Using LLMs allows us to have much higher coverage than if we didn't use it. To me and our engineering team, this is a pretty good thing because in the time prioritizat…

Have you systematically tested this approach? It sounds like you are reporting on your good vibes. Your writing is strictly anecdotal.

I’ve been working with AI, too. I see what I’m guessing is the same unreliability that you admit in the last part of your article. For some reason, you are sanguine about it, whereas I see it as a serious problem.

You say you aren’t complacent, but your words don’t seem to address the complacency issue. “More tests” does not mean better testing, or even good enough testing.

Google “automation bias” and tell me what policies and procedures or training is in place to avoid it.

Re: Using LLMs to enhance our testing practices

#18

Post by swatcoder »

renegade-otter wrote: post #14

In every single system I have worked on, tests were not just tests - they were their own parallel application, and it required careful architecture and constant refactoring in order for it to not get out of hand. "More tests" is not the goal - you need to write high impact tests, you need to think about how to test the most of your app surface with least amount of test code. Sometimes I spend more time on the test co…

Fully agreed.

It's bad enough when human team members are submitting useless, brittle tests with their PR's just to satisfy some org pressure to write them. The lazy ones provide a false sense of security even though they neglect critical scenarios, the unstable ones undermine trust in the test output because they intermittently raise false negatives that nobody has time to debug, and the pointless ones do nothing but reify architecture so it becomes too laborious to refactor anything.

As contextually aware generators, there are doubtless good uses for LLM's in test developement, but (as with many other domains) they threaten to amplify an already troubling problem with low-quality, high-volume content spam.

Re: Using LLMs to enhance our testing practices

#19

Post by sesm »

apwell23 wrote: post #15

i would love to used to use it change code in ways that compiles and see if test fails. Coverage metric sometimes doesn't really tell you if some piece of code is covered or not.

Coverage metric can tell if lines of code were executed, but they can't tell if execution result was checked.

Re: Using LLMs to enhance our testing practices

#20

Post by viraptor »

renegade-otter wrote: post #14

In every single system I have worked on, tests were not just tests - they were their own parallel application, and it required careful architecture and constant refactoring in order for it to not get out of hand. "More tests" is not the goal - you need to write high impact tests, you need to think about how to test the most of your app surface with least amount of test code. Sometimes I spend more time on the test co…

Pretty much this and I prefer the opposite. "Here's the new test case from me, make the code pass it" is a decent workflow with Aider.

I get that occasionally there are some really trivial but important tests that take time and would be nice to automate. But that's a minority in my experience.

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